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Dimensionality Reduction

Dimensionality reduction is the process of reducing the number of random variables under consideration for training a model.

Analysis Functions

Functions for dimensionality reduction analysis include:

  • Factor Analysis: describes variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors

  • Principal Component Analysis: method to determine the correlation of data points

Selection Functions

Functions for dimensionality reduction selection include:

  • Feature Selection: method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables

References